Near Approach.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import twint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nest_asyncio\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "governorates = pd.read_csv('Governorates.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "gov_names_en = []\n",
    "for gov in governorates['Name']:\n",
    "    gov_names_en.append(gov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = twint.Config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "e.Since = \"2020-03-08\"\n",
    "e.Until = \"2020-03-22\"\n",
    "e.Store_csv = True\n",
    "e.Lang = 'en'\n",
    "for word in gov_names_en:\n",
    "    e.Output = 'Nearby Approach/English/EgyptianEnglishNearbyTweets' + word + '.csv'\n",
    "    e.Near = word + ', Egypt'\n",
    "    twint.run.Search(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "asw = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsAswan.csv', index=False)\n",
    "\n",
    "asw.drop_duplicates(inplace = True)\n",
    "asw.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "asw.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "asw['governorate'] = 'Aswan'\n",
    "\n",
    "asw.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsAswan.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "asy = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsAsyut.csv', index=False)\n",
    "\n",
    "asy.drop_duplicates(inplace = True)\n",
    "asy.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "asy.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "asy['governorate'] = 'Asyut'\n",
    "\n",
    "asy.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsAsyut.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "isk = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsAlexandria.csv', index=False)\n",
    "\n",
    "isk.drop_duplicates(inplace = True)\n",
    "isk.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "isk.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "isk['governorate'] = 'Alexandria'\n",
    "\n",
    "isk.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsAlexandria.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "ism = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsIsmailia.csv', index=False)\n",
    "\n",
    "ism.drop_duplicates(inplace = True)\n",
    "ism.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "ism.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "ism['governorate'] = 'Ismailia'\n",
    "\n",
    "ism.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsIsmailia.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "uqs = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsLuxor.csv', index=False)\n",
    "\n",
    "uqs.drop_duplicates(inplace = True)\n",
    "uqs.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "uqs.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "uqs['governorate'] = 'Luxor'\n",
    "\n",
    "uqs.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsLuxor.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "bah = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsRed sea.csv', index=False)\n",
    "\n",
    "bah.drop_duplicates(inplace = True)\n",
    "bah.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "bah.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "bah['governorate'] = 'Red Sea'\n",
    "\n",
    "bah.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsRed sea.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "jiz = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsGiza.csv', index=False)\n",
    "\n",
    "jiz.drop_duplicates(inplace = True)\n",
    "jiz.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "jiz.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "jiz['governorate'] = 'Giza'\n",
    "\n",
    "jiz.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsGiza.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "sws = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsSuez.csv', index=False)\n",
    "\n",
    "sws.drop_duplicates(inplace = True)\n",
    "sws.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "sws.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "sws['governorate'] = 'Suez'\n",
    "\n",
    "sws.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsSuez.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "fym = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsFayoum.csv', index=False)\n",
    "\n",
    "fym.drop_duplicates(inplace = True)\n",
    "fym.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "fym.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "fym['governorate'] = 'Fayoum'\n",
    "\n",
    "fym.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsFayoum.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "qah = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsCairo.csv', index=False)\n",
    "\n",
    "qah.drop_duplicates(inplace = True)\n",
    "qah.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "qah.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "qah['governorate'] = 'Cairo'\n",
    "\n",
    "qah.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsCairo.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnf = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsMonufia.csv', index=False)\n",
    "\n",
    "mnf.drop_duplicates(inplace = True)\n",
    "mnf.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "mnf.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "mnf['governorate'] = 'Monufia'\n",
    "\n",
    "mnf.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsMonufia.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "mny = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsAl-minya.csv', index=False)\n",
    "\n",
    "mny.drop_duplicates(inplace = True)\n",
    "mny.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "mny.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "mny['governorate'] = 'Al-Minya'\n",
    "\n",
    "mny.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsAl-minya.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "bsw = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsBeni suef.csv', index=False)\n",
    "\n",
    "bsw.drop_duplicates(inplace = True)\n",
    "bsw.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "bsw.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "bsw['governorate'] = 'Beni Suef'\n",
    "\n",
    "bsw.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsBeni suef.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "bsd = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsPort said.csv', index=False)\n",
    "\n",
    "bsd.drop_duplicates(inplace = True)\n",
    "bsd.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "bsd.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "bsd['governorate'] = 'Port Said'\n",
    "\n",
    "bsd.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsPort said.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "sjn = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsSouth sinai.csv', index=False)\n",
    "\n",
    "sjn.drop_duplicates(inplace = True)\n",
    "sjn.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "sjn.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "sjn['governorate'] = 'South Sinai'\n",
    "\n",
    "sjn.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsSouth sinai.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmy = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsDamietta.csv', index=False)\n",
    "\n",
    "dmy.drop_duplicates(inplace = True)\n",
    "dmy.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "dmy.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "dmy['governorate'] = 'Damietta'\n",
    "\n",
    "dmy.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsDamietta.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "swj = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsSohag.csv', index=False)\n",
    "\n",
    "swj.drop_duplicates(inplace = True)\n",
    "swj.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "swj.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "swj['governorate'] = 'Sohag'\n",
    "\n",
    "swj.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsSohag.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssh = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsNorth sinai.csv', index=False)\n",
    "\n",
    "ssh.drop_duplicates(inplace = True)\n",
    "ssh.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "ssh.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "ssh['governorate'] = 'North Sinai'\n",
    "\n",
    "ssh.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsNorth sinai.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "ghr = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsGharbia.csv', index=False)\n",
    "\n",
    "ghr.drop_duplicates(inplace = True)\n",
    "ghr.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "ghr.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "ghr['governorate'] = 'Gharbia'\n",
    "\n",
    "ghr.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsGharbia.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "ksh = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsKafr el-Sheikh.csv', index=False)\n",
    "\n",
    "ksh.drop_duplicates(inplace = True)\n",
    "ksh.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "ksh.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "ksh['governorate'] = 'Kafr el-Sheikh'\n",
    "\n",
    "ksh.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsKafr el-Sheikh.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "qna = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsQena.csv', index=False)\n",
    "\n",
    "qna.drop_duplicates(inplace = True)\n",
    "qna.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "qna.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "qna['governorate'] = 'Qena'\n",
    "\n",
    "qna.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsQena.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "mtr = pd.read_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsMatrouh.csv', index=False)\n",
    "\n",
    "mtr.drop_duplicates(inplace = True)\n",
    "mtr.drop(['quote_url','source','user_rt_id','user_rt','retweet_id','retweet_date','translate','trans_src','trans_dest'], axis=1, inplace=True)\n",
    "mtr.drop(['id', 'conversation_id', 'created_at', 'timezone', 'user_id', 'photos', 'cashtags', 'link', 'retweet', 'video','mentions','reply_to'], axis=1, inplace=True)\n",
    "\n",
    "mtr['governorate'] = 'Matrouh'\n",
    "\n",
    "mtr.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweetsMatrouh.csv', index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_nearby_tweets = pd.concat([asw,asy,isk,ism,uqs,bah,jiz,sws,fym,qah,mnf,mny,bsw,bsd,sjn,dmy,swj,ssh,ghr,ksh,qna,mtr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b6182502c8>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(english_nearby_tweets.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_nearby_tweets['language'] = 'English'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_nearby_tweets.to_csv('Nearby Approach/English/EgyptianEnglishNearbyTweets.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_nearby_tweets = pd.concat([asw,asy,isk,ism,uqs,jiz,sws,fym,qah,bsw,bsd,dmy,swj,ksh,qna])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "arabic_nearby_tweets['language'] = 'Arabic'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "nearby_tweets = pd.concat([arabic_nearby_tweets, english_nearby_tweets])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "nearby_tweets['method'] = 'Nearby'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>username</th>\n",
       "      <th>name</th>\n",
       "      <th>place</th>\n",
       "      <th>tweet</th>\n",
       "      <th>urls</th>\n",
       "      <th>replies_count</th>\n",
       "      <th>retweets_count</th>\n",
       "      <th>likes_count</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>near</th>\n",
       "      <th>geo</th>\n",
       "      <th>governorate</th>\n",
       "      <th>language</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>20:25:39</td>\n",
       "      <td>ahmed_anwar_el7</td>\n",
       "      <td>الحلنجى</td>\n",
       "      <td>Nile Corniche Aswan | كورنيش النيل بأسوان</td>\n",
       "      <td>اخدنا يوم حلو بعد اذن نهاية العالم 🙏 pic.twitt...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>[]</td>\n",
       "      <td>Aswan, Egypt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aswan</td>\n",
       "      <td>Arabic</td>\n",
       "      <td>Nearby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>19:10:01</td>\n",
       "      <td>muhammedaaref</td>\n",
       "      <td>المُررْ 🖤</td>\n",
       "      <td>Aswan Downtown</td>\n",
       "      <td>#NewProfilePic\\nيعم اهوو بنضححكك 😁💜 pic.twitte...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>['#newprofilepic']</td>\n",
       "      <td>Aswan, Egypt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aswan</td>\n",
       "      <td>Arabic</td>\n",
       "      <td>Nearby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>22:16:43</td>\n",
       "      <td>bodymontaser11</td>\n",
       "      <td>پودي منتصر </td>\n",
       "      <td>Nile Corniche Aswan | كورنيش النيل بأسوان</td>\n",
       "      <td>يا قلبى يا وحيد مع حلمك البعيد.. pic.twitter.c...</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>[]</td>\n",
       "      <td>Aswan, Egypt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aswan</td>\n",
       "      <td>Arabic</td>\n",
       "      <td>Nearby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>21:56:53</td>\n",
       "      <td>khaledhelmy27</td>\n",
       "      <td>🦇</td>\n",
       "      <td>Aswan Old Market | سوق اسوان القديم</td>\n",
       "      <td>الوضع بقي حرج pic.twitter.com/NbXo0eUsFu – at ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>[]</td>\n",
       "      <td>Aswan, Egypt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aswan</td>\n",
       "      <td>Arabic</td>\n",
       "      <td>Nearby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>17:13:04</td>\n",
       "      <td>a_saeedrashd</td>\n",
       "      <td>Rashd, Ahmd saeed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#اسوان\\nمن أكثر الأماكن الدخلت قلبي سريعا ♥️ p...</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>['#اسوان']</td>\n",
       "      <td>Aswan, Egypt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aswan</td>\n",
       "      <td>Arabic</td>\n",
       "      <td>Nearby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18698</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>02:02:50</td>\n",
       "      <td>mai_elsherbeny9</td>\n",
       "      <td>ℳ𝒶𝓎ℴ𝓊𝓈𝒽.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#new_profile_pic  +1 A very well spent day💯 pi...</td>\n",
       "      <td>[]</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>152</td>\n",
       "      <td>['#new_profile_pic']</td>\n",
       "      <td>Matrouh, Egypt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Matrouh</td>\n",
       "      <td>English</td>\n",
       "      <td>Nearby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18699</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>02:01:56</td>\n",
       "      <td>mohammedelomdaa</td>\n",
       "      <td>Иломда</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charlie 🖤\\n#NewProfilePic pic.twitter.com/iAiS...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>['#newprofilepic']</td>\n",
       "      <td>Matrouh, Egypt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Matrouh</td>\n",
       "      <td>English</td>\n",
       "      <td>Nearby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18700</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>02:01:45</td>\n",
       "      <td>hannaragab</td>\n",
       "      <td>Hana Ragab</td>\n",
       "      <td>NaN</td>\n",
       "      <td>If we’re able to express the heaviness held in...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>Matrouh, Egypt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Matrouh</td>\n",
       "      <td>English</td>\n",
       "      <td>Nearby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18701</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>02:01:14</td>\n",
       "      <td>cocowawa__</td>\n",
       "      <td>كريمه.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I deserve.</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>Matrouh, Egypt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Matrouh</td>\n",
       "      <td>English</td>\n",
       "      <td>Nearby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18702</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>02:00:17</td>\n",
       "      <td>asmaaa_atef</td>\n",
       "      <td>بلقيس</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Please choose the right way only.</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>Matrouh, Egypt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Matrouh</td>\n",
       "      <td>English</td>\n",
       "      <td>Nearby</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180599 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date      time         username               name  \\\n",
       "0      2020-03-21  20:25:39  ahmed_anwar_el7            الحلنجى   \n",
       "1      2020-03-21  19:10:01    muhammedaaref          المُررْ 🖤   \n",
       "2      2020-03-19  22:16:43   bodymontaser11       پودي منتصر    \n",
       "3      2020-03-19  21:56:53    khaledhelmy27                  🦇   \n",
       "4      2020-03-19  17:13:04     a_saeedrashd  Rashd, Ahmd saeed   \n",
       "...           ...       ...              ...                ...   \n",
       "18698  2020-03-08  02:02:50  mai_elsherbeny9           ℳ𝒶𝓎ℴ𝓊𝓈𝒽.   \n",
       "18699  2020-03-08  02:01:56  mohammedelomdaa             Иломда   \n",
       "18700  2020-03-08  02:01:45       hannaragab         Hana Ragab   \n",
       "18701  2020-03-08  02:01:14       cocowawa__             كريمه.   \n",
       "18702  2020-03-08  02:00:17      asmaaa_atef              بلقيس   \n",
       "\n",
       "                                           place  \\\n",
       "0      Nile Corniche Aswan | كورنيش النيل بأسوان   \n",
       "1                                 Aswan Downtown   \n",
       "2      Nile Corniche Aswan | كورنيش النيل بأسوان   \n",
       "3            Aswan Old Market | سوق اسوان القديم   \n",
       "4                                            NaN   \n",
       "...                                          ...   \n",
       "18698                                        NaN   \n",
       "18699                                        NaN   \n",
       "18700                                        NaN   \n",
       "18701                                        NaN   \n",
       "18702                                        NaN   \n",
       "\n",
       "                                                   tweet urls  replies_count  \\\n",
       "0      اخدنا يوم حلو بعد اذن نهاية العالم 🙏 pic.twitt...   []              0   \n",
       "1      #NewProfilePic\\nيعم اهوو بنضححكك 😁💜 pic.twitte...   []              0   \n",
       "2      يا قلبى يا وحيد مع حلمك البعيد.. pic.twitter.c...   []              1   \n",
       "3      الوضع بقي حرج pic.twitter.com/NbXo0eUsFu – at ...   []              0   \n",
       "4      #اسوان\\nمن أكثر الأماكن الدخلت قلبي سريعا ♥️ p...   []              2   \n",
       "...                                                  ...  ...            ...   \n",
       "18698  #new_profile_pic  +1 A very well spent day💯 pi...   []              3   \n",
       "18699  Charlie 🖤\\n#NewProfilePic pic.twitter.com/iAiS...   []              0   \n",
       "18700  If we’re able to express the heaviness held in...   []              0   \n",
       "18701                                         I deserve.   []              0   \n",
       "18702                  Please choose the right way only.   []              1   \n",
       "\n",
       "       retweets_count  likes_count              hashtags            near  geo  \\\n",
       "0                   0           24                    []    Aswan, Egypt  NaN   \n",
       "1                   0            8    ['#newprofilepic']    Aswan, Egypt  NaN   \n",
       "2                   0           37                    []    Aswan, Egypt  NaN   \n",
       "3                   0            3                    []    Aswan, Egypt  NaN   \n",
       "4                   0            7            ['#اسوان']    Aswan, Egypt  NaN   \n",
       "...               ...          ...                   ...             ...  ...   \n",
       "18698               2          152  ['#new_profile_pic']  Matrouh, Egypt  NaN   \n",
       "18699               0            6    ['#newprofilepic']  Matrouh, Egypt  NaN   \n",
       "18700               2            2                    []  Matrouh, Egypt  NaN   \n",
       "18701               0            2                    []  Matrouh, Egypt  NaN   \n",
       "18702               0            0                    []  Matrouh, Egypt  NaN   \n",
       "\n",
       "      governorate language  method  \n",
       "0           Aswan   Arabic  Nearby  \n",
       "1           Aswan   Arabic  Nearby  \n",
       "2           Aswan   Arabic  Nearby  \n",
       "3           Aswan   Arabic  Nearby  \n",
       "4           Aswan   Arabic  Nearby  \n",
       "...           ...      ...     ...  \n",
       "18698     Matrouh  English  Nearby  \n",
       "18699     Matrouh  English  Nearby  \n",
       "18700     Matrouh  English  Nearby  \n",
       "18701     Matrouh  English  Nearby  \n",
       "18702     Matrouh  English  Nearby  \n",
       "\n",
       "[180599 rows x 16 columns]"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nearby_tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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